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Database replication helps keep data available and allows systems to handle more read requests.

Database replication enhances data availability and read performance but has challenges like replication lag.

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

Kafka uses replication and in-sync replicas to ensure data is always available.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Read-after-write consistency ensures users see their latest updates immediately.

Primary shards partition data, while replica shards duplicate it for redundancy.

Elasticsearch uses master election and quorum to prevent split brain scenarios.

Distributed locks ensure only one server performs a task, avoiding duplication.

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

Replicas keep your system available, while snapshots allow for data recovery.

Elasticsearch uses shards for data distribution and replicas for redundancy.

You can replay Kafka events safely by separating live and replay processes.

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

Kafka consumer reprocessing involves safely replaying events to correct system states.

Consistency models determine what data reads in distributed systems can see.

Failover ensures systems remain operational by managing leader changes during failures.

Redis is a fast in-memory data store used for caching and low-latency applications.

matchIndex tracks what each follower has, while commitIndex shows what is committed.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Kafka can help ensure messages are processed once, but requires careful design.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Choosing the right distributed lock depends on your system's needs and existing tools.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Retry storms occur when too many retries overload a failing service.

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

Poison messages in Kafka can cause infinite retries, risking system stability.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Distributed caching spreads data across multiple servers for better performance and reliability.

Consistent hashing helps distribute data across servers with minimal movement.

Stateless servers are easier to scale because they don't store user session data locally.

Classifying failures and using controlled retries in Kafka prevents outages.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

Kafka's `seek()` lets you replay only specific messages, saving time.

Raft consensus helps distributed systems elect leaders and replicate logs reliably.

Jitter helps spread out client retries to avoid traffic spikes.

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

Kafka commit strategies determine how offsets are managed during message processing.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

You can update Elasticsearch indices without downtime using alias switching.

Older offsets can overwrite newer ones in Kafka if not handled properly.

RPC allows one service to call a function in another service over the network.

Consensus ensures that distributed systems can agree on decisions even during failures.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Database per Service ensures each microservice owns its data, reducing dependencies.

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Choosing between a shared database and a database per service affects system design significantly.

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

Sharding splits databases into smaller parts to improve performance and scalability.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

Cache stampede occurs when many requests hit the database due to a cache miss.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.